Introduction | Models | Data preparation | Receiver Locations | Detection Data | Bayesian Analysis | Priors | Model convergence | Model | Convergance and model performance | Posteriors | Plotting

Introduction | Data preparation | Fit the standard COA model | Fit a COA model that allows for receiver- and time-specific detection probabilities | Fit a model that uses detections from a moored test tag to inform detection probabilities | Comparison | References

COA Standard | Gaussian transmitter signal decay | Logistic transmitter signal decay | COA Time Varying | COA Tag-Itegrated

Make a Movebank dataset "frictionless" | Transform a Movebank dataset to Darwin Core | Transform a Movebank dataset to Ecological Metadata Language (EML) | Example dataset

The actel manual has moved elsewhere!

SMRU SRDL tag QC workflow | Output .CSV files | Metadata .CSV file | SSMOutputs .CSV file

Wildlife Computers QC config file structure | Additional details on config parameters

Wildlife Computers (WC) tag QC workflow | Output .CSV files | Metadata .CSV file | ssmoutputs .CSV file

SMRU QC config file structure | Additional details on config parameters

Background | Workflows

Types of environmental data | Usage of the extractMoor() function | Load example dataset | Moorings data source | Find closest mooring to our animal detections | Moorings data download | Creating a depth-time plot using moorings data, and adding detections | Moorings data extraction | Examining the appended environmental data

Getting started | Check the file's metadata | Converting VDAT file to a CSV | Converting a VDAT file to a CSV with time correction | Splitting a VDAT file into a folder of CSVs | Other file types

Background | Common challenges and other resources | Illustration of moveHMM workflow: elk movement analysis | Package features

Index | Before we start | 1.1. Preliminary analysis of acoustic detections using actel | 1.2. Using IMOS data in your analyses | 1.3. Preparing the data from your study area using actel | 1.4. Checking the quality of the input files

Index | 2.1. Exploring the runRSP results | 2.2. Visualizing the runRSP outputs | 2.3. Distances travelled exclusively in-water

List available files | User credentials | Back to the regularly-scheduled programming | Download project or data extraction files | Search and download tag detections | Upload files to MATOS

Types of environmental data | Usage of the extractBlue() function | Load example dataset | Bluelink (BRAN) environmental data extraction | Running the function with full_timeperiod = TRUE | Water temperature at depth (30 m) | Surface 3D current speeds

Data columns | HR2-180 VDAT, n = 25 | ATTITUDE, n = 7 | BATTERY, n = 11 | CFG_CHANNEL, n = 12 | CFG_STATION, n = 9 | CFG_STUDY, n = 7 | CFG_TRANSMITTER, n = 12 | CLOCK_REF, n = 9 | CLOCK_SET, n = 9 | DATA_ERROR, n = 5 | DATA_SOURCE_FILE, n = 5 | DEPTH, n = 7 | DEPTH_STATS, n = 11 | DET, n = 20 | DET_FILTER, n = 3 | DET_SENS, n = 25 | DIAG, n = 12 | DIAG_FAST, n = 10 | DIAG_HR2, n = 15 | EVENT, n = 13 | EVENT_FAULT, n = 9 | EVENT_INIT, n = 9 | EVENT_OFFLOAD, n = 13 | HEALTH_HR2, n = 9 | TEMP, n = 8 | TEMP_STATS, n = 11 | VR2AR VRL, n = 24 | HEALTH_VR2AR, n = 8 | VR2TX VRL, n = 24 | HEALTH_VR2TX, n = 8 | VR2W VRL, n = 26 | DIAG_VR2W, n = 8 | DIAG_VR2W_INTERIM, n = 8 | HEALTH_VR2W, n = 8 | VR2W180 VRL, n = 26

Download the data | Convert VRL using rvdat | Read it into R | See what we've got | Other variables

Row-by-row data description | Exploring the VDAT CSV's data structure | The first row contains metadata about vdat.exe, itself. | The next 20+ lines contain the column names of different subsets of the data. | The next ~7 rows contain receiver configuration | Logged data! | Detections are logged to the tenth of a millisecond | The last three rows are data associated with the download

Using review() | Including previous data | Applying drift

map() | Map an SSM fit to data, using default settings | Control track components | Change projection

Disclaimer | Models | Data | Argos Least-Squares (LS) location data | Argos Kalman Filter (KF) location data | GPS location data | Light-level geolocation data | Projected location data | A mixture of location data types | Fitting a model | Custom data formats | Access results | Visualising a model fit | References

Processing data | Fit an SSM | emf: specifying error multiplication factors | map: fixing model parameters | control: controlling the optimization | References

References

sim() | Simulate tracks from each of the 3 process models | Plot simulated tracks | Fit SSM to simulated lon,lat locations (with Argos error) | sim_fit() | Simulate tracks from an SSM fit and plot: | Simulate tracks with a potential function to help avoid land: | Simulate central place foraging tracks | sim_filter() | route_path() | Simulate tracks from the posterior of SSM fit(s) - sim_post() | References

Getting your files | OTN projects | ACT/MATOS projects | FACT projects | Running the functions | New matches "since" a certain date | Suggestions to improve the push summaries | Errors and how to fix them | References

Required data | What if my tags are detected across many reciever array projects? | Usage of the runQC() function | Accessing the QC'd data and metadata | Visualising the QC'd detections

Parallel | Listing | Downloading | Summarizing

ACT members

Specifying drift stations | Applying drift | drift() function

morts() function | Duration of residence events | 1. last | 2. any | Cumulative residence events | Notes on selecting a method | Output | infrequent() function | 1. recent | 2. defined | Options | Exclude single detections | Previously identified mortalities | Look backwards | Drift and season

mort format | Other formats | actel | glatos | VTrack | Other packages? | Manual formatting

Basic plotting of residence events | Customizing | Interactive plots | Options | Add flagged mortalities | Seasonality | Facetting | Exclude single detections | Source code

Applying seasonality | overlap argument | season() function

stationchange() function | resmax() function | resmaxcml() function

Example | route_path() | re-route Weddell seal SSM-predicted locations

Index | 3.1. Total dynamic Brownian Bridge Movement Model (group dBBMM) | 3.2. Fine-scale dynamic Brownian Bridge Movement Model (timeslot dBBMM) | 3.3. Calculating areas of space use | 3.3.1. Group areas | 3.3.2. Track areas

Index | 4.1. Calculating overlaps for group dBBMM | 4.2. Calculating overlaps for timeslot dBBMM | 4.2.1. Obtain overlap data from timeslot dBBMM for applying further statistical analysis | The end

Index | 5.1. Calculating dBBMM areas and overlaps between pairs of groups in time steps

Data preparation | Model fitting | Model visualisation | State process inference | Covariates | Model checking | Custom plots | Other features | predict functions | knownStates | fit = FALSE | References

Types of environmental data | Usage of the extractEnv() function | Load example dataset | Environmental data extraction | Gap filling functionality | Interpolated sea surface temperature | Modeled ocean surface currents | Examining the appended environmental data

Receiver Array Report | Run Shiny App | Detections map | Detections overview | Transmitters overview | Detections over time | Detections per station | Detections per hour | Array performance | References

Tagging Project | Run Shiny App | Transmitter deployments | Transmitter detections | Detections over time | Abacus plot | Connectivity plot | Detections network map | References

Alaska Harbor Seal Example | Essential Packages | Pre-processing Track Data and Land Polygon Data | Re-Route Raw Argos Observations | Limit Barrier Polygon to Region of Interest | Create Visibility Graph | Determine Re-routed Track via Shortest Path | Update Geometry With Re-routed Coordinates | Re-Routing a Movement Model Prediction | Fit Movement Model and Predict Locations | Identify Predicted Locations on Land | Re-route Model Prediction via Shortest Path | Update Predicted Path | Multiple Imputation | Create Multiple Imputations from {crawl} Model | Space Use Analysis

The Test Environment | Re-route the path | Did it work?

Features | Preliminary Procedures | Data Preparation | TDR Data Representation | Identification of Activities at Various Scales | Calibrated TDR Data Representation | Dive Summaries | Calibrating Speed Sensor Readings | Bout Detection | Summary | Acknowledgements | References

Introduction | momentuHMM overview | Examples | Discussion